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KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications

KES-AMSTA 2011: Agent and Multi-Agent Systems: Technologies and Applications pp 2–15Cite as

Machine Learning and Agents

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6682))

Abstract

The paper reviews current research results integrating machine learning and agent technologies. Although complementary solutions from both fields are discussed the focus is on using agent technology in the field of machine learning with a particular interest on applying agent-based solutions to supervised learning. The paper contains a short review of applications, in which machine learning methods have been used to support agent learning capabilities. This is followed by a corresponding review of machine learning methods and tools in which agent technology plays an important role. Final part gives a more detailed description of some example machine learning models and solutions where the asynchronous team of agents paradigm has been implemented to support the machine learning methods and which have been developed by the author and his research group.

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Authors and Affiliations

  1. Chair of Information Systems, Gdynia Maritime University, Morska 83, 81-225, Gdynia, Poland

    Piotr Jędrzejowicz

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  1. Piotr Jędrzejowicz
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Editors and Affiliations

  1. Manchester Metropolitan University, M1 5GD, Manchester, UK

    James O’Shea  & Keeley Crockett  & 

  2. Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

  3. University of Bournemouth, BH12 5BB, Poole, UK

    Robert J. Howlett

  4. University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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Jędrzejowicz, P. (2011). Machine Learning and Agents. In: O’Shea, J., Nguyen, N.T., Crockett, K., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2011. Lecture Notes in Computer Science(), vol 6682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22000-5_2

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  • DOI: https://doi.org/10.1007/978-3-642-22000-5_2

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